Nutritional assessment of servicemen in relation to area of duty and feeding habits: a Pakistani prospective

The present study was carried out to determine incidence of overweight and obesity in Pakistani servicemen with reference to their area of duty, feeding habits and also to identify risk factors. Accordingly, 2,501 servicemen selected from all over Pakistan using multiple stage stratified sampling protocol. Nutrition assessment performed using body mass index (BMI), waist to hip ratio (WHR) and dietary assessment using food frequency questionnaire. Collected data was analyzed using the SPSS version 25. Regression was used to find risk factors of obesity and WHR. Results indicated that about 1/4th of servicemen were smokers. Approximately, 1/5th of them were overweight and about one quarter were eating fruits and vegetables for <3 days/ week and <4 days/week, respectively. Only 1/3rd of them were physically active for at least <40 minutes per day. Age and fruits intake were significantly predicting BMI with a direct relation and vegetable intake was negatively correlated to BMI of the servicemen. Age and rank were significant predictors of WHR while, physical activity was negatively correlated to WHR. It is concluded and suggested from our study that there is a need to modify eating patterns and habits as well as improving physical activity on daily basis for healthy and long life of the servicemen.

Systematic review of metrics used to characterize dietary nutrient supply from household consumption and expenditure surveys

Objective: To review existing publications using Household Consumption & Expenditure Survey (HCES) data to estimate household dietary nutrient supply to (1) describe scope of available literature, (2) identify the metrics reported and parameters used to construct these metrics, (3) summarize comparisons between estimates derived from HCES and individual dietary assessment data, and (4) explore the demographic and socioeconomic sub-groups used to characterize risks of nutrient inadequacy. Design: This study is a systematic review of publications identified from online databases published between 2000 to 2019 that used HCES food consumption data to estimate household dietary nutrient supply. Further publications were identified by “snowballing” the references of included database-identified publications. Setting: Publications using data from low- and lower middle income countries Results: In total, 58 publications were included. Three metrics were reported that characterized household dietary nutrient supply: apparent nutrient intake per adult-male equivalent per day (n=35), apparent nutrient intake per capita per day (n=24), and nutrient density (n=5). Nutrient intakes were generally overestimated using HCES food consumption data, with several studies finding sizeable discrepancies compared to intake estimates based on individual dietary assessment methods. Sub-group analyses predominantly focused on measuring variation in household dietary nutrient supply according to socioeconomic position and geography. Conclusion: HCES data are increasingly being used to assess diets across populations. More research is needed to inform the development of a framework to guide the use of and qualified interpretation of dietary assessments based on these data.

Validating dietary assessment tools with energy expenditure measurement methods: Is this accurate?

Having an accurate dietary assessment tool is a necessity for most nutritional studies. As a result, many validation studies have been carried out to assess the validity of commonly used dietary assessment tools. Since based on the energy balance equation, among individuals with a stable weight, Energy Intake (EI) is equal to Energy Expenditure (EE) and there are precise methods for measurement of EE (e.g. doubly labeled water method), numerous studies have used this technique for validating dietary assessment tools. If there was a discrepancy between measured EI and EE, the researchers have concluded that self-reported dietary assessment tools are not valid or participants misreport their dietary intakes. However, the calculation of EI with common dietary assessment tools such as food frequency questionnaires (FFQs), 24-hour dietary recalls, or weighed food records, is based on fixed factors that were introduced by Atwater and the accuracy of these factors are under question. Moreover, the amount of energy absorption, and utilization from a diet, depends on various factors and there are considerable interindividual differences in this regard, for example in gut microbiota composition. As a result, the EI which is calculated using dietary assessment tools is likely not representative of real metabolizable energy which is equal to EE in individuals with stable weight, thus validating dietary assessment tools with EE measurement methods may not be accurate. We aim to address this issue briefly and propose a feasible elucidation, albeit not a complete solution.

Evaluating Mobile Apps For Dietary and Food Timing Assessment For Use in Research and Clinical Assessment (Preprint)

UNSTRUCTURED Abstract: Over the last decade, health apps have become an increasingly popular tool utilized by clinicians and researchers to track food consumption and exercise. However, as consumer apps have primarily focused on tracking dietary intake and exercise, many lack technological features to facilitate the capture of critical food timing details. To determine a viable app that recorded both dietary intake and food timing for use in our clinical study, we evaluated the timestamp data, usability, privacy policies, accuracy of nutrient estimates, and general features of 11 mobile apps for dietary assessment. Apps were selected using a keyword search of related terms and the following apps were reviewed: Bitesnap, Cronometer, DiaryNutrition, DietDiary, FoodDiary, FoodView, Macros, MealLogger, myCircadianClock, MyFitnessPal, and MyPlate. Our primary goal was identifying apps that record food timestamps, which 8 of the reviewed apps did (73%). Of those, only 4/11 (36%) allowed users to edit the timestamps, an important feature. Next, we sought to evaluate the usability of the apps, using the System Usability Scale (SUS) across 2 days, with 82% of the apps receiving favorable scores for usability (9/11 apps). To enable use in research and clinic settings, the privacy policies of each app were systematically reviewed using common criteria with 1 Health Insurance Portability and Accountability Act (HIPAA) compliant app (Cronometer). Furthermore, protected health information is collected by 9/11 (81%) of the apps. Lastly, to assess the accuracy of nutrient estimates generated by these apps, we selected 4 sample food items and one researcher’s 3-day dietary record to input into each app. The caloric and macronutrient estimates of the apps were compared to nutrient estimates provided by a registered dietitian using the Nutrition Data System for Research (NDSR). Compared to the 3-day food record, the apps were found to consistently underestimate calories and macronutrients compared to NDSR. Overall, we find the Bitesnap app to provide flexible dietary and food timing functionality capable for research or clinical use with the majority of apps lacking in necessary food timing functionality or user privacy.

Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients

Malnutrition is common, especially among older, hospitalised patients, and is associated with higher mortality, longer hospitalisation stays, infections, and loss of muscle mass. It is therefore of utmost importance to employ a proper method for dietary assessment that can be used for the identification and management of malnourished hospitalised patients. In this study, we propose an automated Artificial Intelligence (AI)-based system that receives input images of the meals before and after their consumption and is able to estimate the patient’s energy, carbohydrate, protein, fat, and fatty acids intake. The system jointly segments the images into the different food components and plate types, estimates the volume of each component before and after consumption, and calculates the energy and macronutrient intake for every meal, based on the kitchen’s menu database. Data acquired from an acute geriatric hospital as well as from our previous study were used for the fine-tuning and evaluation of the system. The results from both our system and the hospital’s standard procedure were compared to the estimations of experts. Agreement was better with the system, suggesting that it has the potential to replace standard clinical procedures with a positive impact on time spent directly with the patients.

Multiomics Approach to Precision Sports Nutrition: Limits, Challenges, and Possibilities

Most sports nutrition guidelines are based on group average responses and professional opinion. Precision nutrition for athletes aims to improve the individualization of nutrition practices to optimize long-term performance and health. This is a 2-step process that first involves the acquisition of individual-specific, science-based information using a variety of sources including lifestyle and medical histories, dietary assessment, physiological assessments from the performance lab and wearable sensors, and multiomics data from blood, urine, saliva, and stool samples. The second step consists of the delivery of science-based nutrition advice, behavior change support, and the monitoring of health and performance efficacy and benefits relative to cost. Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition. Another major challenge is the development of evidence-based individualized nutrition recommendations that are embraced and efficacious for athletes seeking the most effective enhancement of performance, metabolic recovery, and health. At this time precision sports nutrition is an emerging discipline that will require continued technological and scientific advances before this approach becomes accurate and practical for athletes and fitness enthusiasts at the small group or individual level. The costs and scientific challenges appear formidable, but what is already being achieved today in precision nutrition through multiomics and sensor technology seemed impossible just two decades ago.

Diet Quality Assessment in Wheelchair Users with Multiple Sclerosis

Background: Diet quality has not been distinctively examined in wheelchair users with multiple sclerosis (MS). Methods: This cross-sectional study examined the Diet History Questionnaire (DHQ) III and the Automated Self-Administered 24-h (ASA24) Dietary Assessment Tool in 128 wheelchair users with MS. Participants were prompted to complete the DHQ-III and 3 ASA24 recalls during a seven-day data collection period. Healthy Eating Index (HEI)-2015 scores were calculated for DHQ-III and ASA24, and scores were compared with normative values. Spearman’s correlation analyses (rs) estimated the associations between DHQ-III and ASA24 HEI-2015 total and component scores with supportive paired sample t-tests. Results: HEI-2015 scores for DHQ-III and ASA24 were significantly higher than normative values for total score, total protein foods, and added sugar. Correlations between HEI-2015 scores generated using ASA24 and DHQ-III were all statistically significant (range rs = 0.23–0.69); however, significant differences between ASA24 and DHQ-III values were noted for HEI-2015 total score, total fruits, whole fruit, total vegetable, greens and beans, whole grains, seafood and plant protein, refined grains, and saturated fats. Conclusion: This study provided a novel description of diet quality in wheelchair users with MS for guiding future research promoting healthy eating in this population.